Microsoft Business Intelligence Advancement Workshop 2008 Download And Install – Open Access Policy Institutional Open Access Program Guidelines for Special Issues Editorial Process Research and Publication Ethics Article Processing Ethics Awards Statements
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Editors’ Choice articles are based on recommendations from scientific journal editors from around the world. The editors select a small number of recently published articles in the journal that they believe will be of particular interest to readers or important in the area of research in question. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.
Received: 23 May 2022 / Revised: 19 June 2022 / Accepted: 21 June 2022 / Published: 22 June 2022
Monitoring animal behavior over time is important for zoos and aquariums that want to constantly assess animal welfare. Although new digital tools make behavioral tracking more accessible, timely analysis of behavioral data to derive meaningful insights can be a challenge. Business intelligence software has the potential to help address these challenges. Business intelligence software is a class of tools that combines the ability to integrate multiple data streams with advanced analytics and robust data visualization. Here I highlight the features of the Microsoft Power BI platform as an example. Power BI is the leading choice in business intelligence software and is freely available. To demonstrate the potential of business intelligence tools for behavioral monitoring, I provide two dashboard examples of data captured using ZooMonitor behavioral logging software. The first dashboard illustrates a simple quarterly behavioral summary to track behavioral change on a continuous basis. Another dashboard visualizes the data related to the enrichment assessment. It is my hope that this introduction to business intelligence software and the Microsoft Power BI platform will provide zoo and aquarium researchers and managers with new tools to support their evidence-based decision-making processes.
Learning, Teaching And Student Experience 2022, 24 25 May, Belfast
Animal welfare is a dynamic process and its evaluation must be dynamic as well. The development of ongoing behavioral monitoring programs in zoos and aquariums is a valuable tool for identifying significant changes in behavior and enabling proactive animal management. However, continuous analysis of observational behavioral data introduces unique challenges compared to traditional hypothesis-driven behavioral studies over fixed time periods. Here I present business intelligence software as a potential solution. Business intelligence software combines the ability to integrate multiple data streams with advanced analytics and robust data visualization. As an example, I review the Microsoft Power BI platform, the leading choice in business intelligence software available for free. With Power BI, users can incrementally apply data cleansing and shaping, then build dashboards using a library of visualizations through a drag-and-drop interface. I’m sharing two example dashboards built with Power BI using data from the ZooMonitor behavioral logging app: a quarterly behavioral summary and an enrichment evaluation summary. I hope this introduction to business intelligence software and Microsoft Power BI will empower researchers and managers working in zoos and aquariums with new tools to improve their evidence-based decision-making processes.
Increased attention to animal welfare, possibly driven by both ethical and regulatory reasons, has made monitoring animal welfare a core task of many animal-related organizations [1]. Although there are many definitions of animal welfare [2, 3, 4, 5], there is general agreement that welfare can vary along a continuum and can change over time. Thus, well-being can be viewed as a dynamic process [6]. For applied welfare practitioners in zoos, aquariums and laboratories, welfare assessment is often done in an effort to initiate husbandry changes that can improve welfare. These actions then lead to the need for additional welfare evaluations. Because well-being is a dynamic process, our methods for assessing well-being must be equally dynamic. Ultimately, zoos and aquariums require welfare monitoring systems to be dynamic and support evidence-based decision-making.
Behavioral monitoring programs, in which animal behavior is systematically documented in a continuous manner, are an attractive approach to animal welfare assessment [7]. The importance of animal behavior as an indicator of well-being is well recognized [3], and it has been shown that behavior is the most frequently evaluated measure of well-being [8]. Unlike traditional hypothesis-driven animal behavioral research, where behavior is typically recorded over a fixed period of study, often with experimental treatments, behavioral monitoring programs seek to continuously monitor behavior over time to establish individual baseline patterns of behavior. Trends and deviations in these patterns can then be monitored to proactively identify potentially significant changes in well-being. Recently, new digital tools have simplified the collection of behavioral data, allowing users to quickly record animal behavior using mobile devices [9]. Although these new tools have simplified data collection, they have not solved many of the inherent challenges of rapidly analyzing large amounts of data and reporting findings, which is necessary due to the dynamic process of welfare monitoring.
Analysis and reporting of data from behavioral monitoring programs differs from traditional hypothesis-driven research in several important ways. The most fundamental difference is that the data from the behavioral monitoring program is ongoing without a defined study period, after which the data would be analyzed. Furthermore, behavioral monitoring programs may begin without a clear question to guide the analyses. In addition, behavioral monitoring programs often focus on applied decision-making, with a need for quick, actionable insights. Due to the lack of leading questions and the need for timely data analysis, the statistical approach may differ between both types of data collection, with behavioral monitoring programs often relying more on exploratory data analysis to identify meaningful patterns, as opposed to inferential analyses. which are common to traditional animal behavior research. Finally, there is a key difference in how results are shared, with behavioral monitoring programs typically generating reports that are shared internally within the organization with managers, while traditional research is typically shared through peer-reviewed publications and presentations at scientific conferences. Therefore, applied animal welfare monitoring often requires different analysis tools than those typically used in traditional research to address these unique analytics needs.
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In this paper, I present potential solutions in the field of business intelligence (BI) software. First, I provide a brief overview of the history of BI and its modern use. Then I highlight the available BI option as an example, Microsoft Power BI. Finally, I illustrate how behavioral data from the ZooMonitor behavioral monitoring software can be integrated with Microsoft Power BI to provide robust analytics for applied welfare monitoring. The aim of this paper is to introduce managers and scientists who want to closely monitor animal welfare, who may currently rely on spreadsheet applications such as Microsoft Excel for their BI needs, to the available BI solutions they can implement to inform their decision-making processes. Although a step-by-step guide is beyond the scope of this paper, my goal is to provide the reader with sufficient knowledge of the key concepts necessary to get started with BI software. Adopting new tools can require a large investment of time and energy and can be daunting. I hope to convince the reader of the transformational potential these available BI tools can have in maximizing the impact of our monitoring efforts on animal welfare.
The term “business intelligence” is believed to have been first recorded by Richard Miller Devens in 1865, used to describe how a banker was adept at following the news to exploit current events for personal and financial gain [10]. Almost 100 years later, in 1958, Hans-Peter Luhn later used the term when he proposed a system that could scan documents to extract and analyze text to obtain “business intelligence” [11]. Our modern view of BI is usually attributed to Howard Dresner, who coined BI in 1989 as an umbrella term to describe the growing set of computerized technologies that aided in “fact-based decision making” [12]. A popular modern definition of BI is that of Wickom and Watson [13]: “Business intelligence is a broad category of technologies, applications and processes for collecting, storing, accessing and analyzing data to help their users make better decisions. Although our contemporary views of BI have largely evolved and expanded beyond traditional business contexts (e.g. healthcare [14, 15, 16] and education [17, 18]), the basic concept of analyzing information, often from diverse sources, to make an informed decision, he remained.
Given the interdisciplinary nature of BI, it is therefore not surprising
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